One-Carbon Metabolism,<i>MTHFR</i>Polymorphisms, and Risk of Breast Cancer
Bibliographic record
Abstract
Accumulating evidence from epidemiologic studies suggests that risk of breast cancer is reduced in relation to increased consumption of folate and related B vitamins. We investigated independent and joint effects of B vitamin intake as well as two polymorphisms of a key one-carbon metabolizing gene [i.e., methylenetetrahydrofolate reductase (MTHFR) 677C>T and 1298A>C] on breast cancer risk. The study uses the resources of a population-based case-control study, which includes 1,481 cases and 1,518 controls. Significant inverse associations between B vitamin intake and breast cancer risk were observed among non-supplement users. The greatest reduction in breast cancer risk was observed among non-supplement users in the highest quintile of dietary folate intake [odds ratio (OR), 0.61; 95% confidence interval (95% CI), 0.41-0.93] as compared with non-supplement users in the lowest quintile of dietary folate intake (high-risk individuals). The MTHFR 677T variant allele was associated with increased risk of breast cancer (P, trend = 0.03) with a multivariate-adjusted OR of 1.37 (95% CI, 1.06-1.78) for the 677TT genotype. The 1298C variant allele was inversely associated with breast cancer risk (P, trend = 0.03), and was likely due to the linkage of this allele to the low-risk allele of 677C. The MTHFR-breast cancer associations were more prominent among women who did not use multivitamin supplements. Compared with 677CC individuals with high folate intake, elevation of breast cancer risk was most pronounced among 677TT women who consumed the lowest levels of dietary folate (OR, 1.83; 95% CI, 1.13-2.96) or total folate intake (OR, 1.71; 95% CI, 1.08-2.71). From a public heath perspective, it is important to identify risk factors, such as low B vitamin consumption, that may guide an effective prevention strategy against the disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".